Overview
- Brings together recent data mining applications of a machine learning technique
- Covers a wide range of domains demonstrating the utility of the Learning Classifier Systems technique
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Computational Intelligence (SCI, volume 125)
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Table of contents (10 chapters)
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Front Matter
Keywords
About this book
Just over thirty years after Holland first presented the outline for Learning Classifier System paradigm, the ability of LCS to solve complex real-world problems is becoming clear. In particular, their capability for rule induction in data mining has sparked renewed interest in LCS. This book brings together work by a number of individuals who are demonstrating their good performance in a variety of domains.
The first contribution is arranged as follows: Firstly, the main forms of LCS are described in some detail. A number of historical uses of LCS in data mining are then reviewed before an overview of the rest of the volume is presented. The rest of this book describes recent research on the use of LCS in the main areas of machine learning data mining: classification, clustering, time-series and numerical prediction, feature selection, ensembles, and knowledge discovery.
Editors and Affiliations
Bibliographic Information
Book Title: Learning Classifier Systems in Data Mining
Editors: Larry Bull, Ester Bernadó-Mansilla, John Holmes
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-540-78979-6
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2008
Hardcover ISBN: 978-3-540-78978-9Published: 30 May 2008
Softcover ISBN: 978-3-642-09775-1Published: 30 November 2010
eBook ISBN: 978-3-540-78979-6Published: 01 July 2008
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: IX, 230
Topics: Mathematical and Computational Engineering, Artificial Intelligence